Peer-to-Peer (P2P) agent communication over a defined network structure is described by Gossip Algorithms [1]}, [2]}. Considering a network with a known structure that might change over time, pairwise communication of agents is proposed to replace a centralized network structure. Decentralized Collaborative learning based on Gossip Algorithms [3]} leverages a known network structure where neighbors are agents learning similar models. This method uses an asynchronous update phase, where parameters are collected from neighbors and used to update a local model. This approach allows for some individuality in models but is restricted to networks where a structure is known. Smoothing terms in optimization and knowledge propagation that is agnostic of model structure prevent this model from applying to our problem. Since nonlinear models learn a different granularity of the data in each layer, a global knowledge transfer term is too vague.
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